Influence of Accelerometer Position on Gearbox Fault Severity Classification through Evaluation of Deep Learning Models

Gears are key elements in mechanical transmission systems. Fault diagnosis in gearboxes is a cutting-edge topic nowadays mainly addressed by machine learning approaches. The success classifying a fault under this approach depends directly on the quality of the information provided to the models, and in gearboxes, quality of captured information depends on the place where a sensor is located. In this work, we propose a deep learning approach for the evaluation of the best of two accelerometers positions for classifying nine severity levels in gearboxes. Based on the performance of LSTM models whose hyperparameters have been found by a Bayesian optimization, we show which one is the best source of information for this layout. Also, we have performed statistical comparisons in order to find any statistical differences between models and accelerometers.